Abstract
Federated Learning is a Privacy-Preserving Alter-Native for Distributed Learning with No Involvement of Data Transfer. as the Server Does Not Have Any Control on Clients' Actions, Some Adversaries May Participate in Learning to Introduce Corruption into the Underlying Model. Backdoor Attacker is One Such Adversary Who Injects a Trigger Pattern into the Data to Manipulate the Model Outcomes on a Specific Sub-Task. This Work Aims to Identify Backdoor Attackers and to Mitigate their Effects by Isolating their Weight Updates. Leveraging the Correlation between Clients' Gradients, We Propose Two Graph Theoretic Algorithms to Separate Out Attackers from the Benign Clients. under a Classification Task, the Experimental Results Show that Our Algorithms Are Effective and Robust to the Attackers Who Add Backdoor Trigger Patterns at Different Location in Targeted Images. the Results Also Evident that Our Algorithms Are Superior Than Existing Methods Especially When Numbers of Attackers Are More Than the Normal Clients.
Recommended Citation
P. Ranjan et al., "Robust Federated Learning Against Backdoor Attackers," IEEE INFOCOM 2023 - Conference on Computer Communications Workshops, INFOCOM WKSHPS 2023, Institute of Electrical and Electronics Engineers, Jan 2023.
The definitive version is available at https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10225922
Department(s)
Computer Science
Keywords and Phrases
backdoor; Federated learning; robustness; tar-geted attackers
International Standard Book Number (ISBN)
978-166549427-4
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2023
